Intelligent Autonomous Robot for Hazardous Material

Collection and Containment

First Semester Final Report 12-7-04

ME 423 Group 5 Jibu Abraham

Stephen Mezzo Ben Bishop

Tracy Shigemura

We pledge our honor that we have abided by the Stevens honor system.

Table of Contents: Project Background …………………………………………………… 3 Project Overview …………………………………………………… 4 Physical Design …………………………………………………… 5 Containment System …………………………………………………… 5 Material Selection …………………………………………………… 11 Design for Manufacturability …………………………………………………… 12 Flow Analysis/Pump Sizing …………………………………………………… 15 Picking a Pump …………………………………………………… 15 Sensors/Control Hardware …………………………………………………… 16 Sonic Range Finder …………………………………………………… 16 Digital Compass …………………………………………………… 17 Infrared Sensor …………………………………………………… 17 CMUcamera …………………………………………………… 18 Wheel Encoder …………………………………………………… 18 Chemical Sniffer …………………………………………………… 19 Programming …………………………………………………… 21 Program Architecture …………………………………………………… 21 Search Logic and the TSP …………………………………………………… 23 Mapping Algorithm …………………………………………………… 23 Search Algorithm …………………………………………………… 28 Clean Algorithm …………………………………………………… 28 Obstacle Avoidance Algorithm …………………………………………………… 28 Language …………………………………………………… 29 Robot Control Concepts …………………………………………………… 29 Labjack Concept …………………………………………………… 30 Brainstem Concept …………………………………………………… 31 Motors …………………………………………………… 33 Sensor Placement …………………………………………………… 35 Weight and Cost Estimation …………………………………………………… 36 Works Cited …………………………………………………… 38 Appendices …………………………………………………… 40

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Project Background

In a New Jersey factory in 1961, the General Motors introduced the world’s first industrial robot. Since that time, robots have served countless roles in numerous fields. Robots are used everywhere from industrial settings to space exploration to disposing of bombs. They can perform a variety of tasks faster, safer, longer, with fewer mistakes, and often more cost effectively than their human counterparts. Yet despite their widespread use and tremendous value, there is still a significant unmet demand for the field of robotics. These demands stem from the most significant limiting factor of a robot’s function, and that is its reliance on human input. That is, a conventional robot is only able to perform its specified task in a constant, pre-determined environment with pre-determined objects with which to work. It has no way to perceive its surroundings and adapt to changes in its environment. Even a small change could cause a failure to achieve its objective. Simply put, robots were dumb. In 1970, SRI International introduced Shakey, the first mobile robot controlled by artificial intelligence. This development, combinedrefinement of robots, opened the door to new areas for robotics.

Autonomy is defined as “Not controlled by others or by oindependent”. It is the issue of autonomy which poses the greatesthe greatest potential, in the field of robotics today. An autonomoto assist in micro- and nanosurgeries, perform more advanced resexploration, save lives in dangerous military situations, and perfodangerous tasks, such as hazardous material clean-ups. On the sutasks a conventional robot would be able to perform, and in somedo. One example of this is the US military’s Predator drone. The unmanned aircraft that performs reconnaissance flights cheaper aflights or satellites. A limiting factor of the Predator is that all datmust be reviewed by intelligence analysts before any conclusionsthat can take days. If an autonomous Predator could reduce this tiimprove intelligence gathering, but potentially save lives. In the lspotted Osama bin Laden during a time when the US was lookingagainst him. He was not identified until it was too late, and the opis this type of perception and intelligent decision making that woautonomous system to perform tasks conventional robots current

The US military’s interest in autonomous technology spans beyond unmanned flight. In March 2004, the Defense Advanced Research Project Agency (DARPA) held its Grand Challenge competition. The object of the competition was to build an autonomous land vehicle that could negotiate a 142-mile course through the Mojave Desert. Despite the millions of dollars spent on development of the vehicles, the results were disappointing. Of the 15 finalists who competed in the competition, not a single vehicle even came close to

An industrial robotic arm

with the constant

utside forces; t challenge, and holds us robot would be able earch in space rm a variety of rface, these seem like limited cases, currently Predator is an nd safer than manned a gathered by the drone can be drawn, a process me, it would not only ate 1990’s, a Predator to launch an air strike portunity was missed. It

uld enable an ly cannot.

A Grand Challenge competitor

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completing the course. Two entrants reached approximately 7 miles, the furthest of any vehicle. Only 7 of the entrants managed to reach the 1-mile mark. The money and time invested into the Grand Challenge by the competitors and the government demonstrates the perceived importance and potential of autonomous robotics.

The idea of using a robot to reduce the human risk in dangerous situations is nothing new. For years, law enforcement agencies and the military have made use of robots to dispose of explosive devices. The examples cited above were also developed with aeye on reducing human risks. In recent years, research and development has begun on robots that can be used to deal with hazardous material incidents. This interest has grown tremendously with the recent anthrax attacks and fears of chemical or bio-terrorism. Currently, the majority of hazmat robots under development focus on tele-robotically identifying and locating hmaterial spills. They are remote controlled vehicles mounted with chemical sniffers to identify the presence of chemical, biological, or nuclear contaminants. A significant portion of this research, however, is directed more on the detection mechanism than the robot itself. Because these robots are not controlled by autonomous systems, there is littleinnovation in this aspect of the designs. Yet due to the threat of chemical or bio-terrorithe chemical sensors must now be able to detect a wider variety of dangerous materiand this is where much of the research has focused. The government is currently developing systems to detect and identify a wide variety of chemical, biological, andnuclear materials, including projects at the Los Alamos and Sandia National Laborato

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azardous

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roject Overview

The initial task that was laid out for the team was to create an intelligent autonom vehicle

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A remote controlled hazardous material

detection robot

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ous vehicle that would be able to optimally complete a desired task. Thewas to be something simple such as a lawnmower or vacuum cleaner. In these cases the task would be to mow the lawn or vacuum the floor in an intelligent maner. The decisionwas made to design an intelligent vacuum. Instead of designing the whole vacuum from scratch the team purchased a Roomba. The Roomba is a relatively inexpensive robotic vacuum cleaner with very limited intelligence. By using the Roomba as a physical platform it allowed the group to spend more time on the design of the program and integration of the sensors then on the physical design of a whole robot. This is a hugadded benefit because the team is comprised of all mechanical engineers with very limited programming and electronic experience. With this large learning curve, too focus on the physical design would take away numerous hours of learning time that we need for the programming and sensor integration side of the project. Also, having the Roomba on hand allowed us to study the autonomous algorithms of the Roomba and trto improve upon them.

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Once we decided on creating a more intelligent vacuum and using the Roomba as the platform, we wanted to create a more unique design problem besides just making it more intelligent. From this notion the Hazbot was born.

The Hazbot is an intelligent autonomous robot that searches, detects and contains hazardous material. The hazardous material gives the project a different spin on things. Instead of just having a robot vacuum that cleans the whole room, with the Hazbot, all that needs to be cleaned is the areas that contain the hazardous material. Not only does the Hazbot have to clean material, but it has to be able to detect the material as well. Because the material that the Hazbot will be cleaning is hazardous, safety now becomes the biggest design issue that we are faced with. Also the containment of the material allows us to have a unique physical design problem, seeing how the rest of the robot does not need to be designed. With the added uniqueness of the project the group feels that it will be quite challenging and demanding of our skills as mechanical engineers and as engineers in general.

The problem was broken up into four distinct parts. There is a physical section which involves the design of the containment system and sensor mountings. The sensing section involves picking sensors to meet the needs of the project and deciding where to mount them. The decision making section involves creating flow charts to describe how the program is going to think, and then the design of specific algorithms that will solve the various problems that arise. And finally, the command section integrates the sensors and the motors together and allows them to be controlled through programming. Physical Design

The physical design of the system consisted of two major tasks. The first was to develop a component that would collect and contain the hazardous material. The second task was to design mountings for the sensor placement. Because the original project consisted of redesigning a vacuum cleaner, the team kept with the idea of using a vacuum for cleaning the material. The first step in the design of the containment system was to define some customer needs for the system and then interpret those needs into design criteria. See Appendix A for the needs and design specifications. Containment System General Architecture

The robot’s physical containment system will be built off of the Roomba vacuum

cleaner. There were several main areas of concern regarding the general design which all concepts must address to be considered. They are as follows:

- The containment system must be incorporated into the Roomba

architecture. This means that the size and weight of the container must be of appropriate dimensions, and there must be access for electrical leads to the Roomba power supply.

- Low cost of manufacturability. Weight: 2/3 - Low level of human interaction. Weight: 3/3

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- Low lift weight of removable containment system. Weight: 1/3 - Ease of use - minimal number of steps that need to be taken for

each use from preparation to material disposal. Weight: 3/3 - Low risk of contamination. The hazardous material must not re-

enter the environment at any point during the Hazbot’s operation. The focal point of this concern is during the removal and disposal of the container or bag being used to contain the hazardous material. Weight: 3/3

Two main types of containments systems were considered. These were disposable

units, which were sealed and disposed of as a whole unit, and reusable units, which were cleaned and decontaminated after each use. Several concepts were created to meet our design needs and each was scored from 1-5, with 5 representing “design exceeds needs” and 1 representing “design does not meet needs”.

Concept 1: All-in-One Cleanable

This is a reusable container concept. After the material collection is complete, the entire containment system (orange piece), consisting of the collection, storage, and vacuum components, would be sealed and removed. It would then be taken to an appropriate facility for disposal of the hazardous material decontamination.

Concept 2: Disposable Bag In this design, as the hazardous material is collected it is stored in a removable

piece of the collection device, similar in concept to a vacuum bag. At the completion of the material collection, the sealed bag is removed and disposed of.

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Concept 3: Cleanable Modular In this concept, the collection and storage components are separate from the

vacuum pump. After the hazardous material is collected, the front portion containing the collection and storage components will be sealed and removed to be cleaned for reuse. The vacuum pump does not have to be removed from the system. (See image below) Concept 4: Disposable Modular

This concept shares its physical architecture with Concept 3. That is, the collection and storage modules are separate from the vacuum unit. After the hazardous material is collected, the front portion (again, excluding the pump) of the container will be sealed, removed, and disposed of. (See image below)

Modular Design Concepts

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Weight

Concept 1

Concept 2

Concept 3

Concept 4

Low Cost 2 2 2 2 3 Low Level of Human Interaction 3 1 2 1 3 Low Lift Weight 1 1 4 3 3 Ease of Use 2 2 2 2 4 Low Risk of contamination 3 2 2 2 3 TOTAL 18 24 20 35

Concept Scoring Table

After some analysis, it was determined that the reusable concepts would not be

cost effective. For the reusable designs, you would need to pay someone to clean up the hazardous waste and spend money on expensive cleaning solvents to make sure that the container is totally clean. Even though in the last concept the whole container will be thrown away, the cost would be lower due to the ease in disposing of the entire sealed container.

Among the disposable designs, Concept 4 had the lowest human interaction because there was no direct contact between any person and the hazardous material. Removing the bag in Concept 2 is a more complicated procedure than removing the entire piece. The other concepts each required cleaning which would bring a person into direct contact. From this matrix, it was determined that the design would move forward as a disposable modular containment system. Material Trapping

Once the above general concepts were narrowed down to one, we then proceeded

to address specific needs of ensuring that the hazardous material is “trapped” once collected so that it does not re-enter the environment during additional collection and removal of the containment system. Some key needs would include:

- Low trap manufacturability cost. Weight: 2/3 - Low level of human interaction needed. Weight: 3/3 - Low risk of material re-contamination. Weight: 3/3 - Ease of assembly. Weight: 1/3 - Low amount of flow restriction. Weight: 2/3

Concept 1: A curved feature will be protruded from bottom of the inside of the

container near the entry way to make sure that materials that enter will not flow back out if tipped.

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Air flow

Particulates

Concept 2: A shutter-like design that is forced open by the vacuum pump, but closes due to gravity when the pump is off. It would allow for one-way flow of materials.

Air flow

Concept 3: A flap trap that would essentially be several U-shaped flaps cut out of a thin sheet of plastic. As with the shutter design, the force of the vacuum pump will pull

the flaps open when on, and gravity will pull them closed when the pump is off.

Air flow

Concept 4: A cap that would be placed at the mouth of the container after

material collection is complete. This concept developed from the fear that particles may stick to the inside walls of the device and fall out once the pump is off.

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Concept 5: A slit design that would need to be manually opened or closed. It

consists of two plates with slits cut out. When the vacuum is operating, the open areas would be aligned. When the vacuum was off, the plates would be slid out of alignment.

Air flow

Weight Concept 1

Concept 2

Concept 3

Concept 4

Concept 5

Low cost 2 4 1 4 3 2Low level of human interaction 3 4 4 4 2 2Low risk of re-contamination 3 3 2 1 3 2Ease of assembly 1 4 1 3 4 2Low Flow restriction 2 2 2 2 4 3TOTAL 37 25 30 33 24

Concept Scoring Table

The main issue with these concepts was the possibility of particles sticking to the

inside of the collection device ahead of the trap. In this event, they could easily be knocked loose and be re-introduced to the environment. From the concept scoring of the material trap design, the team decided to use a combination of Concept 1 (curved feature) and Concept 4 (cap) because this combination offered the greatest functionality and was easily integrated into the design.

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To address the possibility of material re-entering the atmosphere at the back end of the containment device, a hepa filter (high-efficiency particulate air filter) will be placed between the vacuum pump and the collection area. This will make sure no material can exit through the air outlet. The filter will be changeable so that if at any point it becomes clogged, a new one can be put in place to allow for optimal airflow. Hepa filters are designed to remove 99.97% or greater of Dioctyl Phthalate particles with a diameter of .3 um from an air stream. Material Selection

It was decided that the container would be made by injection molding. The types of plastics considered were selected based on existing hazardous-material-housing containers. Poly containment basins are required for most hazardous material storage devices.1 The materials considered and their properties are listed below:

Polyethylene (PE): One of the most commonly used plastics. This thermoplastic has exceptional resistance to physical and chemical attacks of a wide array - from wet to dry, from industrial chemicals to food products. It is translucent - slightly off white to creamy yellow. It is inexpensive, ductile, and low strength.

High Density Polyethylene (PE-HD): Harder and stronger than PE, heavier, less ductile. It can be molded, machined, and joined together by welding. It usually appears wax-like and opaque. UV stabilization can improve its weather resistance but turns the color black. Low cost. Low Density Polyethylene (PE-LD): Lower in strength, density, hardness, stiffness, and cost compared to PE, but has better ductility. Opaque in color - usually used for packaging foils and bags. Resistant to water, moisture, organic solvents and chemicals. Not resistant to those with aromatic or chlorine content. Low cost.

Polyvinyl Chloride (PVC): A medium-strong transparent material that is heavy and stiff. It can be softened by adding softeners to end up with rubber-like materials. Softeners also increase manufacturability. It has superior resistance to acids and bases, and most chemicals, but is susceptible to some solvents. It has poor weather resistance but can be improved by additives. Low cost.

Glycolised Polyester (PETG): Amorphous sheets of plastic that has many features similar to PVC such as resistance and durability. Usually used in applications where thermoformability is required. It is tough, clear, easy to work with, and chemically resistant. Low cost

Polycarbonate (PC): An amorphous plastic with high impact strength (considered fracture-proof), decent ductility, and stiffness. Good outdoor resistance but turns

1 http://www.designinsite.dk/htmsider/inspmat.htm http://www.matweb.com

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yellow after prolonged exposure to sunlight. PC is resistant to oil, fats, and alcohol. Not resistant to other solvents, hot water decreases ductility. High cost.

Polystyrene (PS): An amorphous, low strength, brittle plastic. Hard and stiff. It is not weather resistant - not suitable for outdoor use, is resistant to water, acids, bases, detergents, no solvents. It is transparent in color. Low cost.

Polypropylene (PP): Ductile, low strength, soft material. Stiffness and strength can be improved by using reinforcement. It is similar to HDPE but stiffer. PP catches fire and burns easily but additives can reduce its flammability. It is resistant to water, moisture, acid, bases, and some solvents. Low cost.

Polyurethane (PUR): Types vary greatly from stiff to soft. Excellent outdoor performance and resistant to most acids and solvents. High cost.

Cost Density Flammability UL94

Tensile Strength

Linear Mold Shrinkage

PE-MD $1.627/kg .9 kg/L V-O 20 Mpa .008-.011 cm/cm PE-HD $1.627/kg .96 kg/L HB V-O 10-50 Mpa .003-.02 cm/cnm PE-LD $1.447/kg .9 kg/L HB 7-25 Mpa .02-.03 cm/cm

PVC $1.989/kg 1.38 kg/L V1, V-O 17-52 Mpa .004-.035 cm/cm

PETG 1.27 kg/L 51 Mpa .0035 cm/cm

PC $6.511/kg 1.2 kg/L HB 54-72 Mpa .003-.0075 cm/cm PS $1.985/kg 1.05kg/L HB 25-69 Mpa .004-.006 cm/cm PP $1.447/kg .09kg/L HB 20-80 Mpa Min .006 cm/cm PUR $6.33/kg varies

Our needs require that a material be stable under many conditions - indoor and out,

for a variety of chemicals. It would need to be strong to withstand impact, and finally, low cost if possible. The material chosen was high-density polyethylene. See Appendix B for a list of chemicals that polyethylene containers are commonly used for, and the level of its stability. Design for Manufacturability / Detailed Design Detailed drawings of parts and assembly of design can be found in Appendix C. Container before manufacturability had been addressed:

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The containment system will be manufactured as four main pieces so that components can be injection molded. The first and second components are mirror images of each other. They are displayed below.

Injection molding problem area

The entryway of the container had originally been curved for aesthetic purposes,

so that it would follow the round contours of the Roomba. However, to make it injection moldable, the curvature (see drawing above) was eliminated. The hepa filter will slide into the back end of the containment housings before they have been assembled. To seal the hepa filter so no particles can escape from the side, the group had a concept of sealing the interface between the filter and the container with silicone. Before this concept is set in stone, more research needs to be completed regarding the hepa filter.

Next, an extra lip feature was added so that the two mating sides could be ultra-sonically welded together (for thermoplastics). Ultrasonic welding is a permanent means to join two plastic pieces together. This is done by creating high frequency mechanical vibration. The entire process takes less than two seconds and can be completely automated. The bond that results is uniform and strong, but attention needs to be focused on the design of the joint itself. There

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are several common joints identified in references2 and the one that we chose to use was the Shear Joint. The ultrasonic welding was chosen over an o-ring sealing method. If the containment system was designed to be sealed with an o-ring around the container then this would complicate and lengthen the assembly process. An ultrasonic welding unit costs approximately $18,000 which would save money in the long run as opposed to the o-ring sealing method. Lastly with ultrasonic welding, the part would be 100% leak proof at the mating line, which is a very important design consideration. Also, the design was modified to make sure that uniform cross-sections existed wherever possible.

The last two components will house the vacuum pump. They were also split up because of injection molding issues. Screw bosses were added to mate the top and bottom pieces, and the pump sits right inside of the housings. A mount for the fan has not yet been created because we have not obtained the mounting specifications for the pump at this time. The pump housing has guides on the front for easy placement of the containment system in a dovetail design for good stability. The housing will be secured to the Roomba using two #6 self-tapping screws and the top housing will also use the same screws. Vents were created on top to allow the air from the pump to escape. The maximum material volume that can be contained is 0.7 gallons (165 cubic inches). This volume is a good size and will allow the Hazbot to clean up a decent amount of material in one use.

2 http://www.dow.com/styron/design/guide/welding.htm

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Final assembly of all the parts can be seen Appendix C.

Flow Analysis and Pump Sizing

For this design a pump was needed to turn it into a vacuum. Detailed fluid mechanics calculations were done to pick a pump that would work in the range that was needed at maximum efficiency (See Appendix D for calculations). Bernoulli’s equation was used to calculate the losses associated with the design. Input fields are indicated in yellow. By varying flow rate and filter k-values, a graph was constructed that indicates how flow rate and head losses vary as k changes. Other minor losses and k-values were calculated using a reference text3 and part geometry. Picking a Pump Flow rate and filter k-values were varied and plotted in excel (See Appendix E). The pump that was chosen was the Chiaphua Components Limited (CCL) Vacuum Cleaner Motor - V5 series. This pump was chosen because it was in the specified range for the kind of flow rates and vacuum head we determined. Other requirements were that it was a DC motor, and that it had the appropriate size dimensions (See Appendix F). The pump’s performance curves were given, and this curve was plotted onto one of our system performance curves previously obtained to find an optimal operating point, as shown in the figure below.

3 Fluid Mechanics, 5th edition, Frank M. White, ©2003 pp 389, 390

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From this graph, we can see that at maximum efficiency, the pump would run at a flow rate of about 38 cfm, 32% efficiency, 160 Watts input power, and a pressure of 26 in H20.

As we went through the process of sizing a pump, it became necessary to change part geometry to accommodate pump specifications and the size/shape of the pump. The Hepa filter k-value will be 7. We will need to find a filter that fits our requirements. Sensors and Control Hardware

Sensors are the most important aspect for creating a robot. Picking the right sensors for the tasks needed is key to creating a robot that works correctly and is easy to integrate. Below are the criteria that were used to pick the sensors for the Hazbot.

Field of view and range: How much of the world it can reliably measure - FOV is degrees horizontally and vertically, range is how far into the distance it can measure. Accuracy, repeatability and resolution: How correct is the measurement, how often does it reach the same reading, how precisely can it measure. Responsiveness in the target domain: How well does the sensor work in the environment in which the robot is supposed to work? Most sensors have environments they don't work well in. Power consumption: Sensors are major drain of power. Generally passive sensors use less power than active. There is a trade off between locomotion ability and sensor capability. Hardware reliability: Sensors often work best (or with reasonable reliability) within a certain range of physical conditions, such as temperature and moisture. Size: Affects the size and design of the robot/is affected by the size of the robot

Using the design criteria the group picked the following sensors for the Hazbot. Sonic Range Finder

The Devantech SRF04 Ranger (SONAR) will be used

for long distance ranging. This ultrasonic ranger has an approximate range of 3" to 10'. This ranger has a logic line used to trigger a pulse, with the echo returned on a second line. It requires minimal power and has a compact, self-

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contained design. This sensor connects to the digital I/O lines of the microcontroller. The SRF04 Timing diagram is shown

at right. Only a short 10uS pulse supplied to the trigger input is needed to start the ranging. The SRF04 will send out an 8-cycle burst of ultrasound at 40khz and raise its echo line high. It then listens for an echo, and as soon as it detects one it lowers the echo line again. The echo line is therefore a pulse whose width is proportional to the distance to the object. By timing the pulse it is possible to calculate the range to the obstruction.

Digital Compass

Devantech digital

compass and specs.

The compass uses the Philips KMZ51 magnetic field sensor, which is sensitive enough to detect the Earth's magnetic field. Two compasses mounted at right angles to each other can be used to compute the direction of the horizontal component of the Earth's magnetic field.

Infrared Sensor

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The Sharp GP2D12 IR Sensor will be used on the Hazbot for short distance ranging. The sensors will act primarily as short-range collision detectors (See Sensor Placement in Control section). Due to the placement of the sonar range finder it is possible that, under normal operating conditions, it would be unable to detect an imminent collision. An example of such a situation is when the Hazbot is turning. Because the long-range sensor only points straight ahead, it might not see an obstacle approaching from the side. This sensor takes continuous distance readings and reports the result as an analog voltage. This sensor has a distance range of 10cm (~4") to 80cm (~30"). Camera The CMUcam uses a serial port and can be directly interfaced to other low-power processors such as PIC chips. Mounted on a mobile platform like the Hazbot, the CMUcam is capable of tracking an object, such as our hazardous material. At 17 frames per second, the CMUcam can do the following:

• Track the position and size of a colorful or bright object • Measure the RGB or YUV statistics of an image region • Automatically acquire and track the first object it sees • Physically track using a directly connected servo • Dump complete image over the serial port • Dump a bitmap showing the shape of the tracked object

Since it is impractical to test the Hazbot on real hazardous materials, we will simulate the hazardous material with a red powder. The CMUcam will be used to detect the powder and control the motors to advance the Hazbot toward it. Once the algorithms to accomplish this task are developed, it should be easy to modify them to work with a hazardous material detector instead of the camera

Wheel Encoder

Schematic diagram

and photo of actual Roomba encoder wheel.

Dead reckoning (derived from “deduced reckoning” of sailing days) is a simple

mathematical procedure for determining the present location of an object. A simple example of dead reckoning is tying one end of a rope of specified length to a fixed object

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and walking away. Once you run out of rope, you know you are that specified length away from the object. For mobile robot applications incremental and absolute optical encoders are the most popular type of dead reckoning tool. The simplest type of incremental encoder is a single-channel tachometer encoder, basically an instrumented mechanical light chopper that produces a certain number of sine- or square-wave pulses for each shaft revolution. The Hazbot encoder we will be using has 32 slats, from which we can calculate the resolution. Adding pulses increases the resolution (and subsequently the cost) of the unit. In addition to low-speed instabilities, single-channel tachometer encoders are also incapable of detecting the direction of rotation and thus cannot be used as position sensors. Chemical Sniffer

Within the chemical sensor industry there are three major categories of “dangerous chemicals” which are targeted. The first is military and commercial explosives, covering materials from TNT to black powder. These devices are designed for use in military or security applications, such as in airport baggage screening. The second major category is illicit narcotics, and includes everything from heroin to marijuana. Again, the main target for these sensors is in law enforcement applications. The third major category is toxic chemicals. This category covers materials ranging from dangerous industrial chemicals to weapons grade nerve agents. Some sensors are also capable of identifying organic or biological compounds, and are therefore able to identify potential biological weapons. The vast majority of the chemical sniffers on the market today are able to identify multiple categories of dangerous chemicals, although the two most widely covered are the explosive and narcotics variety. For the case of dangerous nuclear or radioactive materials, a different type of sensor is required. This device is known as a Geiger counter. Our research did not locate any commercially available sensors which combined chemical and nuclear detection capabilities. Based on the nature of these sensors and their uses, it is no surprise that the majority of these devices are either developed by government researchers or are heavily marketed to government agencies. These agencies include the Department of Homeland Security, the Department of Energy, and the Environmental Protection Agency. There are several methods used by chemical sniffers to detect dangerous chemicals in the environment. The two most common methods in commercial sniffers are swabbing and vapor sampling. The swabbing method involves physically rubbing a swab on the surface of the suspicious object. This swab is then analyzed using ion spectrometry to determine the presence of any potentially dangerous chemicals. The vapor sampling method involves taking in ambient air and then analyzing it using ion spectrometry, gas chromatography, or other methods to determine the presence of potentially dangerous substances. Two government developed sensors discovered in our research use different methods for chemical detection. The Los Alamos National Laboratory has developed a system, known as ASPECT (Airborne Spectral Photometric

Carinex SABRE 2000 vapor sampler

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Collection Technology) which uses two sensors mounted on an airplane to identify and map spills. A Fourier Transform Infrared Spectrometer is used to identify the chemical vapors present. An Infrared Line Scanner maps the terrain being searched. The result is a detailed map of the terrain including the location of any hazardous spills. A second method is being developed by the Department of Energy’s Sandia National Laboratories. This sensor uses chemiresistors to detect the presence and intensity of volatile chemicals. The chemiresistors are electrodes coated with polymers which absorb the volatile chemicals and swell, creating a measurable change in electrical resistance. This change in resistance is then used to determine the type and intensity of the material present. The ideal detection method for the Hazbot would be a variation of the ASPECT system being developed at Los Alamos National Laboratory. This system would be able to detect the hazardous material and map the environment, accomplishing our two most difficult tasks with one device. However, because of the considerable cost of such a system and the fact that it is not a commercially available product, it is unreasonable to consider ASPECT as a viable option. The Sandia developed chemical sniffer, although effective, was designed for use in very damp environments, such as underground water supplies and reservoirs. Its ability to detect dangerous chemicals in dry air has not been tested, and therefore eliminates it from our consideration. Among the commercial systems we found, it was determined that a swabbing device would not be practical for our use. The swabbing technique requires either human action or very precise (and expensive) robotic arms to collect samples. It also requires that a specific target be identified to be swabbed, for instance an envelope or briefcase. And even then, you must take the sample swab from a surface that has been in contact with the hazardous substance; swabbing the outside of an envelope full of anthrax might not detect the material. In addition to these functional drawbacks, swabbing devices are often heavy and bulky, as the photo at the right demonstrates. This can make mounting one on our robot a difficult proposition. Swabbing devices are therefore ill suited for use in the Hazbot. Vapor sampling devices are more promising. Many vapor sampling devices are hand-held and easily mountable on the Hazbot. They do not require direct contact with the hazardous material, which means that a substance can be detected even if it is inside a briefcase or envelope. The gas chromatography method used in some vapor sniffers also provides a greater degree of sensitivity and accuracy to its readings. The only drawback to the vapor sensor is that it is difficult to specify the exact location of the material. However, its advantages in functionality far outweigh this drawback and make it the best option available.

Carinex IONSCAN 400M

As for the matter of detecting radioactive materials, there is little differentiation in commercial Geiger counters. There are a considerable number of viable options with very comparable functionalities. All of the commercial Geiger counters we found use the same methods and provide the same degree of accuracy.

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Among the chemical sniffers we found in our research, we chose the Global Security Solutions (GSS) EST-4200 Vapor Tracer as our best fit. Its detection range, broken down by category, is as follows: Military and Commercial Explosives: RDX, PETN, Tetryl, TNT, NG, DNT, Ammonium Nitrate, Black Powder, and others. Illicit Narcotics: Heroin, Cocaine, Marijuana, PCP, Methamphetamines, LSD, THT, and others. Biological, Chemical, and Nerve Agents: Sarin, Soman, Anthrax, Mustard Gas, and others.

The EST-4200 uses Surface Acoustic Wave (SAW) sensors and gas chromatography to identify the hazardous materials. It is sensitive enough to identify even picograms of a hazardous material within ten seconds of asample being taken. It req12-15 minutes to warm up has a battery life of three hours from a self-contained DC battery pack. It has been validated for use by the US Office of National Drug Control Policy, the US military, the EPA, and NASA. Our only concern with the EST-4200 wasits listed weight of 27 lbs. After further review, however, we realized that a majority of this weight was from the component on the left side of the photo. This component hoa display device and some of the analysis hardware. The collection device weighs just over 7 lbs, which should not be difficult to mount. It should not pose a problem to relocate the analysis tools from their current location to a more manageable position on the Hazbot, especially when the display piece, which is not needed, is eliminated. Tare models offered by GSS and other companies which are far lighter, but at the cost of some functionality. They either detect a narrower range of materials, or do not offer the same sensitivity and accuracy. We chose the EST-4200 model as our best-case scenaribecause effectiveness is our primary concern, and its advantages in this category overriany drawbacks we have discovered to this point.

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Global Security Systems’ EST-4200

Programming Architecture

The most important part of any robotic system is its control program. This is the software that controls all of the individual components which make up the robot. Without its control program, a robot would simply be a pile of sensors, wires, and motors. In most conventional robots, the control program gathers data from one or more sensors and then, based on that data, initiates some kind of action. This action could be anything from

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moving the robot to welding parts on an assembly line. The control software is made up of a series of algorithms, each designed to accomplish a specific task. It is the job of the control program to determine which algorithm to run at which time. The method used to decide when to run which algorithm is determined by the program architecture. In order to have a truly autonomous robot, it must be intelligent. The program architecture is what makes a robot intelligent. Although there is currently no true artificial intelligence, there are architectures which allow a robot to demonstrate intelligent behavior. The most widely used method is reactive architecture. This is a stimulus/response type of architecture that can be seen in the Roomba vacuum cleaner. A simple example of this is a robot traveling in a straight line. When the robot runs into a wall, it sets off a bumper sensor. The architecture dictates that the control program must make a decision, and so the robot turns away from the wall. This represents one behavior in the reactive architecture. A wide variety of behaviors can be added, independent of one another, but all depending on some pre-determined stimulus. By observing the Roomba in action, we identified three main behaviors. The first is a simple random operation mode. In this mode, the vacuum moves in a random pattern, sometimes straight ahead, sometimes making turns, cleaning as it goes. In this mode, if the robot encounters an obstacle, either a solid wall, virtual wall, or a step, it will make a turn to avoid the obstacle. The second behavior is a wall-hugging mode. In this mode the Roomba runs parallel to the wall, traveling in small arcs towards the obstacle so that it can follow the path of the wall. This behavior allows the vacuum to clean in and around corners and the edges of obstacles. Both of these behaviors make use of the same three sensors. The first is a bumper. The second is an optical sensor which allows the robot to see virtual walls, or boundaries, set up by the user. The final sensor is an optical sensor which allows the vacuum to sense a drop in elevation caused by a step or other obstacle. The third behavior makes use of a different sensor. A microphone in the dirt collection device alerts the control program when a particularly dirty spot is encountered. When this happens, the Roomba enters its spot-clean mode. This consists of the vacuum slowly traveling in increasingly larger circles until the area is clean. All of these behaviors are initiated by stimuli from the environment and are good examples of the reactive architecture of the Roomba vacuum. Yet, while these behaviors may seem intelligent, they have serious functional flaws. In several trial runs, the Roomba would continuously go over the same areas for lengthy periods of time, and leave larger portions of the room untouched. The Roomba had no idea of where it was or where it had been. It had no idea of what its surroundings looked like or where the dirt was until it traveled over them.

While developing the program architecture for the Hazbot, we made sure to address the flaws we identified in the Roomba’s program. The most critical to our application were the issues of mapping and localization. In order for the Hazbot to safely and accurately search for and locate all spills in a given area, it must have an accurate map and the ability to know where it is and has already been. The first step in developing our control program was to develop our program architecture. In order to do this, we developed flow charts to outline the method of how our robot will think. Below is the general control program flowchart, which is a very basic outline of the Hazbot’s various tasks.

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The first task the Hazbot must complete is creating a map of its surroundings. This must be completed before other tasks are initiated because we do not want to run into the same problems that plague the Roomba vacuum. Once the map is created, the program will develop a search algorithm. Once every point on the map is searched, the Hazbot will initiate its cleaning algorithm. Once all spills are cleaned, the Hazbot will signal that the room is clean.

Search Logic and the Travelling Salesman Problem

Several important aspects of our various algorithms will be based on the well-known traveling salesman problem. The traveling salesman problem (TSP) consists of finding the most efficient way for a “salesman” (our robot) to visit a series of “cities” (search points) on his sales trip. All of the points to be visited are defined, as is the starting point. The path connecting all of the points on the route in the least total distance

On the left is a series of user-defined points for the TSP. The two green points were designated as start and end points. The algorithm generated the path on the right as the optimal route traversing all points between the defined start and finish.

is then determined. There are many different approaches to solving this problem, as it is one of the most significant problems in combinatorial optimization. A solution to the TSP could be applied to countless optimization problems. The shortcoming of existing solutions to the TSP is that as the number of point increases, a large number of solutions can all be close to optimal, but very different from each other. This results in the algorithm taking an exceedingly long and unpredictable amount of time to determine the best solution. Based on tests of several existing algorithms, considering the number of points we will have and the relative simplicity of their arrangement, we do not anticipate the optimization to take more than a matter of seconds.

Mapping Algorithm

When the Hazbot is started, the control program will immediately initiate two functions. The first of these will be the hazardous material detector. This will always be running, even when the search algorithm is not, to insure that the Hazbot will not drive through and spread any spills present. The second function will be the mapping algorithm.

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The development of this algorithm has proved to be the most difficult task to date. The problem stemmed from how to store the data received from the sonar range finder. Our strategy was to keep all aspects of the program as simple as possible. It was believed that the simpler the method, the less chance there was for error. The first method we tried was to use matrices to create the map. The robot would take four readings from the sonar range finer in a north, south, east, west configuration. The dimensions of the matrix would be determined by this initial set of readings. At the distance at which an obstruction was identified, a one would be recorded in the matrix. All other values would be zero. All values in the matrix would be defined based on whether the Hazbot sensed an obstruction in that location. After this initial set of readings, the robot would begin to move in the direction which returned the largest unobstructed distance. As the robot moved, it would continue to take range finder readings at regular intervals and update the matrix accordingly. To insure accuracy, the wheel counters would record the distance the robot had traveled and compare this to the change in the range finder reading in the direction the robot was traveling. As the new readings were taken, the matrix map would be updated accordingly, as illustrated.

Con

until the boualso mean thto test this mwas discovethe initial malready recovery poor. Ia table, therthat area of searched. Fitime to creamethods, w

Afteplan of attacand filling ialgorithm, w

Red = Hazbot location, blue = obstacle, yellow = previously occupied square

tinuing in this manner, the Hazbot would continue moving around the room ndaries of the room were completely defined within the matrix. This would at all obstacles in the room would be defined within the matrix. As we began ethod, we soon discovered its many flaws. The first problem we encountered

red when we made the overall room dimensions larger than those returned in easurements. If we enlarged the matrix, we ran into problems transferring the rded data into a new, larger matrix. Also, the accuracy of such a method was f a small pile of hazardous material was sitting on the floor around the leg of e was a significant chance that because of the obstacle presented by the leg, the matrix would be marked as an obstruction and would not be thoroughly nally, if the room had a complex shape, the matrix method would take a long te the map, and it was often a poor one at that. As we considered other e encountered similar problems. r reexamining our methods, it was decided that the major flaw was with our k. To this point, all of our methods revolved around taking a blank template

n the location of the obstacles based on our sensor readings. While writing the e were forced to set parameters for the template, such as the dimensions of a

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matrix. This was akin to buying carpeting without knowing anything about the floor it will cover. Unless the room fits into a very narrow window of possible sizes and shapes, the carpet will not fit right. As a result, we decided on developing a more dynamic and flexible strategy for creating our map. Instead of trying to map the entire room before beginning a search, the robot will map whatever it can see from its initial position, and update the map every time it moves to another search location. This flexible map not only speeds up the Hazbot’s operation, but also alleviates some of the constraints that we unknowingly put in place with earlier mapping strategies.

The ability of the Hazbot to localize itself in its surroundings is the driving force behind the need for a mapping algorithm. Because of this, we decided to use localization as the starting point in the development of the mapping algorithm. The simplified localization technique the team has chosen is a combination of learned map building and odometric dead reckoning. The Hazbot will traverse the terrain while building a map of the room, obstacles, and location(s) of hazardous material. In order to facilitate the development process, the team has made several assumptions to simplify the problem of localization and avoid a DARPA-like failure.

a) The Hazbot will be tested on an arena that will contain; one obstacle (1’x1’box), one Hazbot, and one red colored object to simulate a hazardous material. The arena shall be painted white.

Hazardous Material

Obstacle

Hazbot

b) Initially the Hazbot will conduct an “Orthogonality check” to check whether

the wheels of the Hazbot are parallel to one face of the walls. This will allow for easier mapping of the room.

c) The hazardous material will be replaced by a colored object which will be

detected using the CMUcam. Efficient indoor mobile robot navigation is limited mainly by the ability of a robot to perceive and interact with its surroundings in a deliberate manner. And, for such interaction to take place, a model or description of the environment usually needs to be specified beforehand. If a global description or measurement of the elements present in the environment is not available, the descriptors and methods for the autonomous building of one are required. That is, either the robot has a global map at its disposal, or it possesses the means to build one. Many systems that incorporate human-made models of the environment have been successfully developed, even when only an approximate map is given or when the system must navigate in crowded environments. However, the

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autonomous building of a global, and possibly dynamic, map of the environment for a mobile robot is still a difficult problem.

In its localization phase, the algorithm determines the robot’s position by correla

e global map as an occupancy grid (a matrix of cells, each h

e initial configuration of the Hazbot arena used fo

ting a local map (generated by a range sensor sweep), with a global map. While the global map can be supplied in advance, this algorithm does not require prior knowledge of the robot’s environment. Instead, it uses sensor data to construct the global map dynamically. The algorithm estimates the robot’s location by comparing the global and local maps. To do so, it computes positions called feasible poses, where the expected view of the robot approximately matches the observed range sensor data. It then selects a best fit from the feasible poses. To create a useful local map, the algorithm requires range measurements in a number of different directions. Such measurements are readily obtained by sweeping the sensor. This sweeping is accomplished by rotating the Hazbot 360 degrees and taking range sensor readings at pre-determined increments. Ensuring that the algorithm identifies feasible poses requires information about the robot's orientation. Orientation can be measured from a digital compass, a gyroscope, or even calculated from the wheel counter data. The team has elected to use a combination of digital compass and wheel counter data.

The algorithm represents thaving a value that indicates whether that cell is empty or occupied). Using its

sensors, the robot determines range vectors in addition to its occupancy value. The occupancy algorithm creates a map of the environment by integrating data collected over time. As the robot explores its environment, information from range sensor sweeps is combined with information about the robot's location to update the occupancy values for the global grid map. Thus the occupancy value for each cell in the grid indicates whether the cell is occupied, empty or unexplored.

The following diagram illustrates thr testing the algorithm.

Hazardous

Obstacle

Hazbot

Material

he Hazbot will first run an algorithm to orient the wheels parallel to one side of the

e

Tarena. This will allow for a cleaner representation of the data in the occupancy grid. Once the wheels are parallel to one side the Hazbot runs a polar ranging algorithm. ThHazbot rotates the sonar sensor them takes readings every 10degrees. This data is then recorded into the occupancy grid as illustrated.

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Distances will be calculated using: x = r * cos(theta), y = r * sin(theta) Once the occupancy grid is created the arena is cut up into areas where range data is available. After the arena is cut a grid of equally spaced points is laid over the area. This is the set up for the team’s traveling salesman algorithm. The Hazbot runs the algorithm to determine an optimal route to traverse every point in the shortest distance. The resolution of the grid is based on the size of the arena.

Data unavailable in

this area

n

While the Haitself in four dthe Hazbot hacontradicts th

Grid Applicatio

Traveling salesman

algorithm in action

zbot is running through the traveling salesman path it is constantly ranging irections (N, S, E, W). The range readings are compared to the global map d created during the initial polar ranging. If a rectangular ranging e existing occupancy grid new sections are added to the grid and the TSA is

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run once again. This new path will only incorporate points not traversed, the Hazbot will not run over the points it has already covered.

New cells added!

This pattern will continue until no new area is discovered and all search points have been visited. For a detailed flowchart of the mapping algorithm, see Appendix G. Search Algorithm

Once the initial occupation grid and traveling salesman route have been established, the control program will initiate the search algorithm. As the robot searches each point on the grid, it will be marked as either clean or contaminated. It will continue in this manner until all points are accounted for, at which point the program will initiate the cleaning algorithm. As described above, the mapping operation will be continuous, continuing to run for the duration of the search period. This integration of the mapping and search operations seems to have solved many of the problems we had encountered in earlier stages of development. For a detailed flowchart of the search algorithm, see Appendix H. Clean Algorithm

After all spills are located, the Hazbot will travel to the nearest spill, again using the traveling salesman algorithm to determine the optimal route. The cleaning process will be similar in function to the Roomba’s spot clean mode. The Roomba starts at the center of the area and cleans outward in a circular pattern. While the circular pattern is an effective way to insure complete cleaning, starting at the center of a spill raises serious contamination risks. Therefore, the Roomba will approach the outermost edge of the outermost square of the spill. It will then move in a circle around the perimeter of the spill, cleaning as it goes. It will proceed in this fashion, steadily decreasing the diameter of its circles until the spill is cleaned. It will then proceed to the next closest spill, and clean it in the same fashion until the room is contaminant free. For a detailed flowchart of the cleaning algorithm, see Appendix I. Obstacle Avoidance In addition to the mission related algorithms, there are several other programs which need to be developed. These include smaller subroutines to run motors and sensors,

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and larger algorithms. The most complicated of these is the obstacle avoidance/wall-hugging algorithm. Similar in concept to the Roomba vacuum’s wall-hugging feature, this will allow the Hazbot to search in corners, along the edges of obstacles, and other tough to reach areas. This mode of operation relies on short-range infrared sensors to determine the distance to a wall or obstacle. If the distance returned by the sensors is less than a specified amount, the robot will turn away from the obstacle until the distance reaches that amount. It will continue moving forward, turning slightly towards the obstacle until the distance is again below the target. This will continue until the robot either covers an area it has already been to or moves away from the obstacle during its natural course. For a detailed flowchart of the wall-hugging algorithm, see Appendix J. Language

We have decided to use Visual Basic as our programming language. We reached this conclusion after studying several languages and considering the hardware and controllers that we would be using. Visual Basic is a very user-friendly language, which was appealing since we do not have an overwhelming programming background. It also works well with the OOPic microcontroller and BrainStem architecture which we are using. It is not as powerful or widely used a language as some of our other options, but because of its smooth integration with our other components and ease of use we decided it would be the best fit. Robotic Control Concepts

Robotic control consists of two main functions. There is the processor which runs the program, and then there is the controller which has input/output capabilities to connect the sensors and motors to the processor. The control of the sensors is dependant on which sensor it is, but mostly all that is needed is some input/output pins with either a digital or analog signal. Motor control is a little more difficult.

For robotic control, motors need more then just a direct power source. To have an effective robot the motors need to be able to change speed, change directions, stop and start numerous times, and be controlled by an extra power source. To do this the motor is connected to a motor driver. This motor driver is made up mainly of an H-Bridge. The H-Bridge allows the motor go forward or in reverse with a combination of a high-low or low-high logic set. To control the speed of the motor the logic inputs are sent to the H-Bridge in a pulse width modulation (PWM). The PWM sends the logic signal at a certain frequency and pulse width. The lower the frequency and thinner the pulse width, the less the average voltage is applied. This allows the voltage output to the motor to vary even though the power input stays the same. Below is a chart showing the PWM signal and how the average voltage changes.

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The typical robotic control system has the processor and controller together in one board which is placed on the robot. The program is downloaded onto the microcontroller board, or PIC board, and the robot is run directly from that. This works very well with most robotic applications but it does have its disadvantages. The first disadvantage of this is the programming languages available. The language of choice for this project is Visual Basic due to its smaller learning curve, power, and user friendliness. Some PIC boards do use Visual Basic syntax but not at the complexity that we need where we can store information and compare information in an array. This could be done using a PIC board programmed in C++, but the learning curve for this language is too steep for mechanical engineers with time constraints. The other disadvantage of using the PIC board is that debugging is not very easy.

With these two considerations the team decided that it would be better to use a laptop as the processor and use a different method to connect the sensors and motors to the laptop. This will allow us to take full advantage of the Visual Basic language and also make the debugging process much easier and visual. We came up with two concepts on how to connect the laptop to the robot. These concepts where then evaluated and one was finalized. The requirements needed for the controller are as follows:

1. The Controller must work with the laptop and be able to run Visual Basic 2. The Controller must have enough IO pins for the various sensors 3. The Controller must have PWM capability to run the motors

Labjack Concept

The first concept we had was to connect the computer to the robot using a data acquisition board (DAQ board). The DAQ board that we decided upon was the Labjack u12. The Labjack u12 has 31 digital I/O pins and 6 analog pins which can be used to connect various sensors and other input/output devices. The Labjack also had numerous

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+5v outputs which is the logic voltage needed for most sensors. This board connected easily to the computer and was relatively easy to control using Visual Basic. Because DAQ boards are not typically used for robotics it was harder then expected to get certain sensors and motors working.

There were no DAQ boards with PWM capability or internal timers, which were needed for ultrasonic sensors. These problems were found through trial and error when attempting to connect everything to the Labjack. To overcome this problem we decided that connecting a microcontroller board, the OOPIC board, to the DAQ board would allow for motor and ultra sonic control. The OOPIC would do all the functions that the Labjack was unable to accomplish and also add more I/O pins if eventually needed. Below is a schematic of how everything would connect in this concept.

After some consideration this concept was determined to be too problematic. Trying to create hardware integrations that had not really been done before can cause many problems in the future. This would also mean that we would need to program the OOPIC board and the Labjack which would make debugging much harder, especially for a handful of mechanical engineers. Also, connecting the OOPIC board to the Labjack was not going to be an easy task and would take up a large number of the IO pins on both boards. We were worried that there would not be enough pins left to connect everything that the project called for. Brainstem Concept

After we determined that the Labjack concept was not exactly what we were looking for, some more research was done to find a different way of connecting the laptop to the robot. After some research a unique microcontroller called the Brainstem was found. The Brainstem has a unique feature called slave mode. In slave mode the sensors and motors connected to the Brainstem could be controlled directly through the serial cable to the laptop. The Brainstem could also act as an independent microcontroller and run without the laptop. In slave mode the Brainstem could be controlled using Visual Basic. However, controlling the Brainstem with Visual Basic is harder then with the Labjack. Other advantages of the Brainstem are the easy integration of motor drivers and the easy addition of other microcontrollers through the I2C

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connection. Bellow is a schematic of how everything would be connected using the Brainstem concept.

The I2C connection is a universal connection that allows two or more

microcontrollers to send data to and from each other. This will allow us to upgrade the system very easily because we can connect numerous controllers to allow for more IO pins. There are two different Brainstem controllers; the first is the Brainstem GP 1.0. This module has 20 digital I/O lines, 8 analog I/O lines, and two I2C connections. The Brainstem Moto1.0 has two dedicated I/O sections designed specifically for PWM motor drivers. For this project we will first use the Brainstem Moto 1.0 controller to first get the motors running. Then using the I2C connection it will be connected to either another Brainstem or an OOPIC board depending on which will work the best for this project. This selection will be made after the motors are working. Most likely it will be the OOPIC board because the group already has the ultrasonic and infrared sensors working with the OOPIC.

The motor drivers picked for this concept are the 3A H-Bridge Module with the LMD 18200 H-Bridge. This driver is designed to fit on the Brainstem Moto and was believed to have enough power to run the motors on the Roomba. Below are the specifications for this motor driver and also a picture of it connected to the Brainstem.

Value Min Typ. Max Unit Motor Voltage 12 12 27.5 (1) V Continuous Motor Current 0 na 3 A Surge Motor Current 0 na 6 A PWM Frequency 0 39,000 500,000 Hz Logic Supply Voltage 4.5 5 5.5 V On Resistance - 0.3 - Ohm

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Motor Input

Power Input

Notes: 1. The H-Bridge can handle up to 55V but an instantaneous change of direction can cause a 2X voltage swing so the effective max is 27.5V.

Based on the pros and cons of each concept the group decided to go with the

Brainstem concept for controlling the robot. The Labjack was a good idea but there was too much reliance on the Labjack and the OOPIC board communicating flawlessly. If the group were unable to make this happen then the project would be a bust. Because the Brainstem is designed specifically for robotic control the group thought that this would be a better choice with less do-or-die options. Motors

The motor/wheel assembly for the Roomba is a very unique design. The wheel is connected to the motor with a rubber gear system which is also connected to the encoder.

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The encoder is then connected to the wheel through a double planetary gear system which resembles an overdrive unit on a car.

In Appendix K there are calculations for finding the gearing ratio of the planetary gear train. From the calculations we found that the encoder rotates 25 times for every turn of the wheel. This makes the encoder very accurate. The gear train also allows for the motor to run a high speeds generating large torque while having the wheels run at much slower speeds.

It was first assumed that getting the motors running would not be a very hard task. After extensive research on motor control and the Brainstem Moto, the group thought that they had enough knowledge to solve any problems encountered. As usual, the assumptions were wrong and getting the motors running is still a problem that is being worked out. One reason why the assumption was wrong was that we had no specifications for the motors. Because the motors that are being used came from the Roomba they were not chosen based on specs. After attempting to get the motors running with the Brainstem for a while the group noticed some writing on the motor and saw that there was a radio shack number for the motors. After searching for the number some startling information came to light. The motor runs at around 1.5 Amps, but depending on the torque for the required load, it needs a maximum of 8 Amps to get the motor started.

The maximum amperage allowed through the motor driver on the Brainstem Moto is 3 Amps. Some good news is that the motors do run when they are connected directly to the power supply. Also, when the motor is connected to the motor driver and a PWM signal is sent to the motor, it will run if you manually give the motor a little kick start. One idea that we are currently working on is connecting a switch directly from the power supply to the motors. When the motors need to start, have an initial kick from the battery start the motor and then switch over to the motor controller so the motor can then be controlled correctly. Another idea is to purchase an H-Bridge that can handle the initial amperage and then design our own motor driver. The first concept is the easier to implement than the second but some testing needs to be done to make sure it will work. If that does not work then we will have to design our own motor driver which will take time and a lot of work to make it correctly.

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Sensor Placement

Sensor placement is a key factor in creating an autonomous robot. All factors must be taken into account so the robot can fully function on its own. There are many different sensors that will be integrated into the Hazbot which will allow it to be autonomous within the initial assumptions. The picture below shows the placement of the sensors with the field of view shown, which is the main factor for the sensor placement.

The sensors with the red and green field of view are the infrared sensors. These will be used as obstacle avoidance similar to the bumpers on the Roomba. The red section of the field of view is the first 10cm where the output voltage of the sensor is the same as longer distances as you can see from the chart below.

If an object was within the first 10 cm of the sensor then it would register as being much farther away. The green is the rest of the field of view up to 40cm, which is where the maximum diameter of the field of view is. To create effective obstacle avoidance with the infrared sensor, the best way is to crisscross two beams in front of the robot.

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If placed correctly, it will cancel out the initial 10cm area and it will also cover the whole front of the vehicle. For the Hazbot this crisscross design was implemented on the front of the containment system. The group worried that because the containment system is farther out then the Roomba body that when the Hazbot is making wide turns it might hit an obstacle in the area in-between the containment system and the Roomba. There are currently two concepts to deal with this that the group is working on. The first concept as seen above is to have two sensors in-between the containment system and the Roomba pointing straight out into the problem area. When the motors and sensors are working next semester the first thing done will be to test this concept. If there are still some areas where the obstacle avoidance is not working then the second concept is to have the crisscross pattern on the two sides as well. We would like to avoid this concept because it will increase the number of infrared sensors from 4 to 6 and would increase the programming time for the obstacle avoidance.

The next sensor placement that is crucial is the placement of the CMUcam. The field of view for the CMUcam with the regular lens is 30 degrees. This was modeled in Pro/e for easier placement of the camera. The placement of this camera is what will drive the number and location of the search points that will be used for the traveling salesmen algorithm. Below is a picture of the initial placement that the group will use. This may change when the group receives the needed feedback from programming. The CMUcam does not detect distances so the field of view of the camera has to remain relatively close to the Hazbot. Another consideration in the placement of the CMUcam was the diameter of the field of view on the floor. This needed to be at least the size of the Roomba so the Hazbot won’t have to hug the walls when searching near them. See Appendix L for detailed views.

The placement of the ultrasonic sensor had only one constraint. It had to be pointing forward to match the logic of the search and localization algorithms. The decision was made to mount the ultrasonic sensor below the CMUcam on the same mounting plate. The various other components, such as the Brainstem, the compass, etc., will be placed inside the Roomba and thus no mounting need be designed for these parts. Weight and Cost Estimation Using the model analysis feature in Pro/e the density of PE-HD was input to obtain the weight and volume. The total weight of all parts, including the Rommba, was 22.7 lbs, which is an acceptable value. The total cost of parts was $884. The cost includes

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all materials, hardware (off the shelf parts), and cost of injection molding. See Appendices O and P for details on the weight and cost analysis.

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Works Cited

“Autonomous.” The American Heritage Dictionary of the English Language, Fourth Edition. 2000. Borenstein, J. “Sensors and Methods for Mobile Robot Positioning.” University of Michigan. URL: http://www-personal.umich.edu/~johannb/shared/pos96rep.pdf (April 96). Breummer, David J. “Intelligent Robots for Use in Hazardous DOE Environments.” Idaho National Engineering and Environmental Laboratory. URL: http://www.isd.mel.nist.gov/research_areas/research_engineering/Performance_M etrics/PerMIS_2002_Proceedings/Bruemmer_Marble.pdf (2002). Defense Advanced Research Projects Agency. “DARPA Grand Challenge.” URL: http://www.darpa.mil/grandchallenge/ (16 September 04). Department of Energy. “A Critical Technology Roadmap.” US Department of Energy.

URL: http://www.sandia.gov/isrc/RIMfinal.pdf (October 98). Federal Emergency Management Agency. “Backgrounder: Hazardous Materials.” URL: http://www.fema.gov/hazards/hazardousmaterials/hazmat.shtm (11 February 03). Garcia, Mario. “Implementation of Three Robotic Control Architectures for Robot Navigation.” Association for Computing Machinery. URL: http://portal.acm.org/citation.cfm?id=767606 (April 03). Grabowski, Bob. “Small Robot Sensors.” URL: http://www.andrew.cmu.edu/user/rjg/websensors/robot_sensors2.html (16 August 02). HazMat Management. “Explosive Demo.” Business Information Group. URL: http://www.hazmatmag.com/article.asp?id=31895&story_id=&issue=06212004& SearchFor=&SearchType=all&RType=&PC= (21 June 04). Hellstrom, Thomas. “From Teleoperation to Autonomy.” Umea University. URL:

http://www.cs.umu.se/kurser/TDBD93/VT03/lectures/ (21 May 03).

Nehmzow, Ulrich. “Mobile Robotics: A Practical Intorduction.” RobotBooks.com. URL: http://www.robotbooks.com/mobile_robotics.htm

Smith, Stephanie. “Experiments in Robotic Software.” National Aeronautical and Space Agency. URL:http://prime.jsc.nasa.gov/ROV/ (3 April 03).

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Vance, Ashlee. “DARPA’s Grand Challenge Proves to be Too Grand.” The Register. URL: http://www.theregister.co.uk/2004/03/13/darpas_grand_challenge_proves/ (13 March 04).

Walker, Joanne. “Intelligent Robotics.” University of Wales. URL: http://users.aber.ac.uk/jnw/CS364/3.php#perception(sensors/perception) (5 February 04).

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Appendix G – Mapping Algorithm Flowchart

Initiate sonar range finder

Rotate 10 deg

Is this a new

position?

Record data to map

Yes

No

Move to next search point

Initiate sonar range finder

Is there new data?

Record data to map

Yes

Rotate 90 deg

No

Is this a new

position?

No

Finish search

Yes

40

Appendix H – Search Algorithm Flowchart

Appendix I – Clean Algorithm Flowchart

41

Appendix J – Obstacle Avoidance/Wall-Hugging Flowchart

42

Appendix D -Flow Rate Calculations H = sum(Kv^2/2g) + f1(L1/D1)(V1^2/2g) + f2(L2/D2)(V2^2/2g) + f3(L3/D3)(V3^2/2g) + f4(L4/D4)(V4^2/2g) + delta(z) Flow rate (gpm) 158.503 Flow rate (cfm) 21.18882 Flow rate (L/s) 10 Flow rate (ft^3/s) 0.353147

K1(sudden contraction) 0.04 K2(90 degree bend) 0.22 K3 (rounded inlet) 0.5 K4 (sudden expansion) 0.44 K5(filter) 11 K6 (sudden expansion) 0.62 K7(exit) 0.5

4 2.2 xsec1 xsec2 8.5 English metric D1(h) 5.44 0.138176 D2(h) A1 [in^2] 34 A2 V1[ft/s] 1.495681 0.455884 V2[ft/s] Re1 4217 Re2 f1 0.039765 f2

1.5 0 xsec4 xsec5 6 English metric D4(h) 2.4 0.06096 D5(h) A4 9 A5 V4[ft/s] 5.650352 1.722227 V5[ft/s] Re4 7028 Re5 f4 0.034129 f5

5 2.05 xsec7 6 English metric

4 3.5 4.5 2.43 xsec3 6 6English metric English metric

4.8 0.12192 D3(h) 2 0.0508

24 A3 27 2.1 0.645835 V3[ft/s] 1.883451 0.574076

5271 Re3 1952 0.037139 f3 0.051359

5 4.14 4.6 1.27 xsec6 6 5.6English metric English metric 5.454545 0.138545 D6(h) 5.05098 0.128295

30 A6 25.76 1.695106 0.516668 V6[ft/s] 1.974114 0.60171

4792 Re6 5168 0.038227 f6 0.037361

Depth Height

Width

43

D7(h) 5.454545 0.138545 A7 30 V7[ft/s] 1.695106 0.516668 Re7 4792 f7 0.038227

MINOR LOSSES H (K1) [m head] 0.000424 H (K2) [m head] 0.004176 H (K3) [m head] 0.008407 H (K4) [m head] 0.066585 H (K5) [m head] 0.149817 H (K6) [m head] 0.011453 H (K7) [m head] 0.00681

MAJOR LOSSES H(1-2) [m] 0.000171 H(2-3) [m] 0.000576 H3(3-4/5) [m] 0.001049 H4(5-6) [m] 0.000395 H5(6-7) [m] 0.000174 H6(7-O) [m] 0.000196 delta z [m] 0.0762

H Total [m head] 0.253519 H Total [feet head] 0.831756 H Total [mm water] 253.5193 H Total [inches water] 9.981076

Power [W] 0.029814

SAMPLE CALCULATIONS Here is a run-through of the calculations done for x-sec 1 and K1.

Major Loss H1: A flow rate of 10 L/s was assumed. Given the x-sectional dimensions, because the shape was roughly a rectangle, the hydraulic diameter was calculated: Dh = 4*A/wetted perimeter = (4*4*8.5)/(4*(4+8.5)) = 5.44 inches or .138 meters Then area: A1 = 4*8.5 = 34 in^2 Then given the flow rate, velocity could be calculated: V1 = Q/A1 = (.353 in^3/s)/(34 in^2) = 1.49 ft/s = .455 m/s

44

Once velocity was known, you can calculate your Reynolds number: Re1 = (rho)(v)(d)/(mu) = (1.205 kg/m^3)(.455 m/s)(.138 m)/(1.8e-5 kg/m*s) = 4217 Finally, a friction value can be calculated: 1/f1^.5 = -1.8log[(6.9/Red) + ((e/d) /3.7)^1.11] given e plastic = .0015mm = .0000015m 1/f1^.5 = -1.8log[(6.9/4217) + ((.0000015/.138) /3.7)^1.11] f1 = 0.039765 The head loss from 1-2 can now be calculated: H1 = f1(L1/D1)(v1^2/2g) = (0.039765)(2.2/5.44)(1.49^2/2*32.2) = 5.5e-4 feet or .000171 m head

Minor Loss K1: K1 is the sudden contraction that occurs at cross section 2. d/D = 4.8/5.44 = .88

From figure 6.22 in Fluid Dynamics, White, Frank, the k value for a sudden contraction can be seen to be .04.

The minor loss from this sudden contraction can be calculated: H (K1) = K1(v^2/2g) = (.04)(.455 m/s)^2/(2*9.8 m/s) = 0.000424 m head

45

Appendix L – Sensor Placement Infrared Sensor

46

CMUcam Placement

47

Appendix A – Needs/Metrics Matrix Need Metric Metric Unit Metric Value Physical

1

The Hazbot battery lasts long enough to clean the entire spill. Time Minutes 100-110 minutes

2

The Hazbot has enough power to run all necessary components. Power draw Watts 125-150 watts

3

The Hazbot vacuum is powerful enough to suck up a wide variety of materials. Flow rate Cubic feet/minute 30-40 cfm

4

The Hazbot is strong enough to resist damage if bumping into obstacles. Force Newtons TBD by testing

5 The Hazbot is easily transportable. Weight of robot lbs 15-25 lbs

6

The Hazbot safely stores the hazardous material.

7

The material storage unit is easily removable and cleanable. Time Minutes < 1 minute

Operational

8 The Hazbot is easy to operate.

9

The Hazbot is inexpensive to operate and maintain. Cost $$ < $50 per use

Performance

10

The Hazbot can filter out very fine materials. Meets HEPA filter standards % of particles to pass <= 0.03% @ 0.3 microns

11

The Hazbot searches and cleans the room in a reasonable amount of time. Speed Hazbot travels TBD by testing

12

The Hazbot avoids driving through a spill and spreading contamination. Range of sensor Feet/inches TBD by testing

13

The Hazbot searches and cleans the room in an efficient manner. Optimized program logic N/A N/A

48

Appendix B – Properties of High Density Polyethethylene

source: http://www.enviroequip.com/catalogs/Catalogue%202004%20PDF/12%20Spill%20Absorbents%20&%20Containment.pdf

49

Appendix M – Patent Search Three major patents were studied.

1. Patent# 6781338 Title: Method and system for robot localization and confinement Assignee: iRobot Corporation Abstract: The present invention discloses a system and method for confining

a robot to a particular space. The system includes a portable barrier signal transmitter that produces a barrier signal primarily along an axis, and a mobile robot capable of avoiding the barrier signal upon detection of the barrier signal. In the preferred embodiment the barrier signal is emitted in an infrared frequency and the robot includes an omni-directional signal detector. Upon detection of the signal, the robot turns in a direction selected by a barrier avoidance algorithm until the barrier signal is no longer detected.4

2. Patent# 6667592

Title: Mapped robot system Assignee: Intellibot Abstract: A method of utilizing a robot system is provided comprising the

steps of commanding the robot system to perform a function in an area, the area having an area layout including at least one area segment. The method further includes accessing by the robot system a stored map of the area layout, the stored map having at least one function task associated with the at least one area segment, localizing a first position of the robot system in the area, determining a function path from the first position of the robot system for navigation of the area and completion of the at least one function task, repeatedly continuously localizing a current position of the robot system while navigating the robot system along the function path, and completing the at least one function task that is associated with the current position of the robot system. 5

3. Patent# 6594844

Title: Robot obstacle detection system Assignee: iRobot Corporation Abstract: A robot obstacle detection system including a robot housing which

navigates with respect to a surface and a sensor subsystem having a defined relationship with respect to the housing and aimed at the surface for detecting the surface. The sensor subsystem includes an optical emitter which emits a directed beam having a defined field of emission and a photon detector having a defined field of view which intersects the field of emission of the emitter at a region. A circuit in

4 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=/netahtml/search-bool.html&r=1&f=G&l=50&co1=AND&d=ptxt&s1=6781338.WKU.&OS=PN/6781338&RS=PN/6781338 5 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=/netahtml/search-bool.html&r=1&f=G&l=50&co1=AND&d=ptxt&s1=6667592&OS=6667592&RS=6667592

50

communication with a detector redirects the robot when the surface does not occupy the region to avoid obstacles. A similar system is employed to detect walls.6

Although there are many existing patents on autonomous mapping and robotic

intelligence, we believe that the mapping/localization algorithm that we create will be unique enough to patent. We also hope to copyright protect our program. 6 http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=/netahtml/search-bool.html&r=1&f=G&l=50&co1=AND&d=ptxt&s1=6594844&OS=6594844&RS=6594844

51

Appendix N – DOT Rules for Hazmat Transport RULES & REGULATIONS Any party that transports hazardous materials in commerce by & to:

1. Interstate, Intrastate, and foreign carriers by rail, car, aircraft, motor vehicle, and vessel

2. Representing that a hazardous material is present in the above vehicles 3. Manufacture, fabrication, marking, maintenance, reconditioning, repairing,

or testing of a package or container that is represented, marked, or certified in use of transportation of hazardous materials.

Must comply with Federal Docket HM-181. Shipper (offerer) responsibilities are:

• HM registration • Hazardous Materials Shipper Responsibilities

- Determination whether the material is “hazardous” or not - Class/Division classification - ID Number - Hazard Warning label - Packaging - Marking - Employee Training - Shipping Papers - Emergency Response Info. - 24 Hour Response Telephone # - Certification - Compatibility - Blocking & Bracing - Placarding Further information and specifics can be found by obtaining a copy of the Docket

from the DOT 49 CFR Parts 100-185.7

7 http://www.ehso.com/DOTHow2Comply.htm

52

Appendix O – Cost Analysis

Cost Estimate Item num Part quantity

unit price

total price

1 Left Containment Housing 1 26.92 26.92

2 Right Containment Housing 1 26.92 26.92

3 Top Pump Housing 1 7.31 7.31 4 #6 self tapping screw 6 0.21 1.25 5 Bottom Pump Housing 1 6.58 6.58 6 Pump 1 0.00 7 Roomba 1 249.99 249.99 8 CMUcam 1 199.00 199.00 9 Ultrasonic sensor 1 34.50 34.50

10 IR sensor 4 2.88 11.50 11 IR front mount 2 1.29 2.57 12 IR Back Mount 2 1.29 2.57 13 4-40 screw 12 0.11 1.37 14 4-40 nut 12 0.02 0.25

153 Devantech Compass 1 49.00 49.00 16 Brainstem 1 79.00 79.00 17 Brainstem Moto 1 69.00 69.00 18 Motor driver 2 42.00 84.00 19 Battery 1 32.00 32.00

Total cost 883.73

53

Appendix P – Part List/Weight Analysis

Weight Estimate

Part Weight (lb)

Left Containment Housing 0.5Right Containment Housing 0.5Top Pump Housing 0.13#6 self tapping screw - Bottom Pump Housing 0.3Pump NA Roomba 13CMUcam 0.24Ultrasonic sensor 0.19IR sensor 0.04375IR front mount 0.325IR Back Mount 0.354-40 screw - 4-40 nut - Devantech Compass 0.03Brainstem 0.2Brainstem Moto 0.2Motor driver 0.2Battery - laptop 6.5Total weight 22.70875

54

Appendix E – Flow Rate and k-value Plot

55

Appendix F – Pump Specifications

56

Appendix K – Gear Ratio Calculations

481812

3

2

1

===

NNN

Superposition Planetary gear solution Arm Gear 1 Gear 2 Gear 3

1 +1 +1 +1 +1 2 0 +4 -48/18 -1

sum 1 5 -1.66 0 Arm rotates 1/5th as fast as the gear 1 which is attached to the encoder Arm is gear 1 on second set of planetary gears; wheel is attached to Arm of second set Therefore encoder rotates 25 times to every one rotation of the wheel. Calculation to convert encoder pulses to linear wheel displacement.

57

Appendix Q – Gantt Chart

58

Appendix C – Assembly Drawings Parts list

Item num Part quantity 1 Left Containment Housing 12 Right Containment Housing 13 Top Pump Housing 14 #6 self tapping screw 65 Bottom Pump Housing 16 Pump 17 Roomba 18 CMUcam 19 Ultrasonic sensor 1

10 IR sensor 411 IR front mount 212 IR Back Mount 213 4-40 screw 1214 4-40 nut 12

153 Devantech Compass 116 Brainstem 117 Brainstem Moto 118 Motor driver 219 Battery 1

59